Here you can find all the deliverables of the UPSCALE project and download the public documents.
D1.1 – Potential for ML-based acceleration in finite volume
Report of the solver algorithm review and further acceleration potential assessment, focusing on the pressure correction step for the acceleration efforts.
D1.2 – ML accelerated aero-thermal solver
Description and definition of the machine learning accelerated aero-thermal solver.
Done by: 30 June 2021
D1.3 – Automatic geometry parameterisation framework
Report describing the characteristics of the automatic geometry parameterisation framework for efficient design space exploration.
D1.4 – Enhanced RANS turbulence model for aero-thermal
Description and definition of all steps taken to define the enhanced RANS turbulence model.
D1.5 – Meso-mechanical crash model
Description and definition of the state-of-the-art battery crash model used in the subsequent tasks.
D1.6 – Battery crash model validation
Report of the battery crash model validation process and conclusions. All steps taken to define the model will be described.
D1.7 – Reduced order battery thermal model
Report including the description and definition of the reduced order model and the conclusions from the validation process.
D1.8 – Data-driven method for optimal partitioning of CFD meshes
Description of the development and implementation of the data-driven method for optimal partitioning of CFD meshes.
D1.9 – Report on model reduction techniques for contact modelling at the micro-, macro-, and meso-scale
Description of the defined model reduction techniques used for contact modelling and conclusions.
D1.10 – AI-based partitioning and proof of concept.
Report on the conclusions of the exploration, analysis and benefits than the use of AI could open for optimal partitioning and in case there are identified benefits, description of the proof of concept and subsequent conclusions.
D2.1 – Requirements for aerothermal simulations reduced order model
This report will list the requirements for the variables necessary to perform the “offline” phase to generate the AI/ROM models. This is in terms of which variables, the size of the dataset and the characteristics to obtain the desired accuracy in every case.
D2.2 – Reduced order models for aerodynamic performance prediction
This delievrable will describe the steps taken to define the reduced order models.
D2.3 – Assessment of reduced order models for aerodynamic performance prediction
Report on the validation process of the previously defined reduced order models used for higher fidelity data.
D2.4 – Validated tool to handle and rationalize aerodynamic data from heterogeneous sources
This report will describe the tool implemented for dealing with all types of aerodynamic data coming from different sources and in different formats.
D2.5 – Assessment of AI/ROM based optimization performances with respect to state-of-the-art methodologies
This report will contain the comparison of the new AI/ROM based technique with the state-of-the-technologies to determine if it results into a better alternative.
3.1 – Load cases and input/output data to generate the reduced order model of the battery model
Description of the load cases and data that will be used to generate the reduced order model of the battery model.
D3.2 – ROM of battery cell for crash assessment
This deliverable will content the definition of a reduced order model of battery cell for crash purposes.
D3.3 – Demonstrator for Virtual Battery support Crash Assessment for collaborative decision making
Benchmark case to prove the implemented methodology developed in WP3 and WP5.
D4.1 – Parametrized exterior geometry and constraints for a fully electric SUV/city car/limousine
This deliverable includes the description of the parametrized exterior geometry and constraints for the specified vehicle.
D4.2 – Reduced order model based on exploring the geometric input parameter space of the parametrized exterior geometry and constraints for a fully electric SUV/city car/limousine
This deliverable will include the description and discussion of results related to reduced order models developed.
D4.3 – Trained machine learning model taking geometrical input parameters and outputting reduced order model coefficients for a fully electric SUV/city car/limousine
This deliverable includes the definition and description of the training machine learning model.
D4.4 – Optimized geometrical input parameters based on the trained machine learning model for a fully electric SUV/city car
This deliverable includes the results of the optimized geometric input parameters for the machine learning model.
D4.5 – Verification of the optimized model with high fidelity simulations for a fully electric SUV/city car and final report on the framework performance
This deliverable describes a report of verification of results with higher fidelity simulations for the specified vehicles and report describing the full framework.
D5.1 – Simulation models with fine resolution for set up training data of the AI model
This deliverable contains the definition of the models that will be used to train the AI model.
D5.2 – Full vehicle model of an e-Golf derivative, adjusted for usage in the project
Numerical model of an e-Golf adapted to the general model of the battery pack for simulation and demonstration purposes.
D5.3 – Full vehicle model of city car, adjusted for usage in the project
Numerical model of city car adapted to the general model of the battery pack for simulation and demonstration purposes.
D5.4 – Corresponding load cases for the full vehicle models to be used within the project
Definition of the load cases for the previous specified vehicles models.
D5.5 – Requirements for setting up a reduced order model of a full vehicle model with parametrized boundary conditions
List of the requirements to define the ROM of the full vehicle according to the desired accuracy.
D5.6 – Requirements for setting up a reduced order model of a battery to be used in a full vehicle crash simulation
List of the requirements for the ROM of the battery that will be used in the full vehicle crash simulation.
5.7 – Requirements for setting up an AI model to improve parallelization of the solver
Report of the requirements for the AI model so that the solver can be parallelized.
D5.8 – Report on methodological approach for battery risk analysis in severe crash scenarios
This delivearble contains the description of the conclusions related to crash impact on batteries.
D5.9 – Final report containing proposal for further use of the new methods
Full report and conclusions to provide the necessary information for the future application of the new method.
D6.1 – Project Handbook
The project handbook will be a live-report of the development of the project. It will be periodically updated.
D6.2 – Quality Plan
The quality plan will define the quality objectives and procedures to be used in the project.
D6.3 – Innovation and Technical Management Plan
Plan of activities in order too coordinate the following aspects:
– Technical developments and innovation.
– Monitoring of the scientific and technological community in what concerns the SoA and trends related to the project.
– Support decision making.
– Favour links to related projects.
D6.4 – Data Management Plan
This data management plant will describe how UPSCALE will contribute to the Open Research Data Pilot. This deliverable will include:
– the handling of research data during & after the end of the project
– what data will be collected, processed and/or generated
– which methodology & standards will be applied
– whether data will be shared/made open access and
– how data will be curated & preserved (including after the end of the project).
This deliverable will be updated over the course of the project whenever significant changes arise.
D7.1 – Plan for the dissemination and exploitation (PEDR)
Description of the plan for the dissemination and exploitation.
D7.2 – Final plan for the dissemination and exploitation (FPEDR)
Description of the plan for the dissemination and exploitation accompanied with the effective outcomes.
D7. 3 – Website
Website containing all the publishable information related to the project. In the deliverable the main website functions will be described.
D7.4 – Report with the compiled requests for publications
Report listing the compiled requests for publications published during the project period.
D7.5 – IPR Strategy Plan
Report of the Intelectual Property Rights plans to assess the patentability of the project results.
D7.6 – Report on dissemination to end-user and stakeholders
Description of the dissemination activities performed to end-users and stakeholders will be detailed in this deliverable.
D8.1 – POPD Requirement No. 1
A description of the security measures that will be implemented to prevent unauthorised access to personal data or the equipment used for processing.